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Solutions to Hackerrank practice problems

This repository contains 185 solutions to Hackerrank practice problems with Python 3 and Oracle SQL.

Updated daily :) If it was helpful please press a star.

• Solve Me First | Problem | Solution | Score: 1
• Simple Array Sum | Problem | Solution | Score: 10
• Compare the Triplets | Problem | Solution | Score: 10
• A Very Big Sum | Problem | Solution | Score: 10
• Diagonal Difference | Problem | Solution | Score: 10
• Plus Minus | Problem | Solution | Score: 10
• Staircase | Problem | Solution | Score: 10
• Mini-Max Sum | Problem | Solution | Score: 10
• Birthday Cake Candles | Problem | Solution | Score: 10
• Time Conversion | Problem | Solution | Score: 15
• Grading Students | Problem | Solution | Score: 10
• Apple and Orange | Problem | Solution | Score: 10
• Kangaroo | Problem | Solution | Score: 10
• Between Two Sets | Problem | Solution | Score: 10
• Breaking the Records | Problem | Solution | Score: 10
• Birthday Chocolate | Problem | Solution | Score: 10
• Electronics Shop | Problem | Solution | Score: 15
• Cats and a Mouse | Problem | Solution | Score: 15
• Forming a Magic Square | Problem | Solution | Score: 20
• Picking Numbers | Problem | Solution | Score: 20
• Climbing the Leaderboard | Problem | Solution | Score: 20
• The Hurdle Race | Problem | Solution | Score: 15
• Intro to Tutorial Challenges | Problem | Solution | Score: 30
• Big Sorting | Problem | Solution | Score: 20
• Pairs | Problem | Solution | Score: 50
• Minimum Absolute Difference in an Array | Problem | Solution | Score: 15
• Marc's Cakewalk | Problem | Solution | Score: 15
• Permuting Two Arrays | Problem | Solution | Score: 40
• The Coin Change Problem | Problem | Solution | Score: 60
• Equal | Problem | Solution | Score: 30
• Sherlock and Cost | Problem | Solution | Score: 50
• Construct the Array | Problem | Solution | Score: 35
• Fibonacci Modified | Problem | Solution | Score: 45
• Laptop Battery Life | Problem | Solution | Score: 10
• Say Hello, World! With Cpp | Problem | Solution | Score: 10
• Arrays - DS | Problem | Solution | Score: 10
• Sock Merchant | Problem | Solution | Score: 10
• Counting Valleys | Problem | Solution | Score: 15
• Jumping on the Clouds | Problem | Solution | Score: 20
• Repeated String | Problem | Solution | Score: 20
• 2D Array - DS | Problem | Solution | Score: 15
• Arrays - Left Rotation | Problem | Solution | Score: 20
• New Year Chaos | Problem | Solution | Score: 40
• Minimum Swaps 2 | Problem | Solution | Score: 40
• Array Manipulation | Problem | Solution | Score: 60
• Hash Tables - Ransom Note | Problem | Solution | Score: 25
• Two Strings | Problem | Solution | Score: 25
• Count Triplets | Problem | Solution | Score: 25
• Frequency Queries | Problem | Solution | Score: 40
• Sherlock and Anagrams | Problem | Solution | Score: 50
• Sorting - Bubble Sort | Problem | Solution | Score: 30
• Mark and Toys | Problem | Solution | Score: 35
• Hash Tables - Ice Cream Parlor | Problem | Solution | Score: 35
• Minimum Time Required | Problem | Solution | Score: 35
• Triple sum | Problem | Solution | Score: 40
• Max Array Sum | Problem | Solution | Score: 20
• Say Hello, World! With Python | Problem | Solution | Score: 5
• Python If-Else | Problem | Solution | Score: 10
• Arithmetic Operators | Problem | Solution | Score: 10
• Python Division | Problem | Solution | Score: 10
• Loops | Problem | Solution | Score: 10
• Write a function | Problem | Solution | Score: 10
• Print Function | Problem | Solution | Score: 20
• List Comprehensions | Problem | Solution | Score: 10
• Find the Runner-Up Score! | Problem | Solution | Score: 10
• Nested Lists | Problem | Solution | Score: 10
• Finding the percentage | Problem | Solution | Score: 10
• Lists | Problem | Solution | Score: 10
• Tuples | Problem | Solution | Score: 10
• sWAP cASE | Problem | Solution | Score: 10
• String Split and Join | Problem | Solution | Score: 10
• What's Your Name | Problem | Solution | Score: 10
• Mutations | Problem | Solution | Score: 10
• Find a string | Problem | Solution | Score: 10
• String Validators | Problem | Solution | Score: 10
• Text Alignment | Problem | Solution | Score: 10
• Text Wrap | Problem | Solution | Score: 10
• Designer Door Mat | Problem | Solution | Score: 10
• String Formatting | Problem | Solution | Score: 10
• Capitalize! | Problem | Solution | Score: 20
• Introduction to Sets | Problem | Solution | Score: 10
• No Idea! | Problem | Solution | Score: 50
• Symmetric Difference | Problem | Solution | Score: 10
• Set add() | Problem | Solution | Score: 10
• Set discard() remove() pop() | Problem | Solution | Score: 10
• Set union() Operation | Problem | Solution | Score: 10
• Set intersection() Operation | Problem | Solution | Score: 10
• Set difference() Operation | Problem | Solution | Score: 10
• Set symmetric_difference() Operation | Problem | Solution | Score: 10
• Set Mutations | Problem | Solution | Score: 10
• The Captain's Room | Problem | Solution | Score: 10
• Check Subset | Problem | Solution | Score: 10
• Check Strict Superset | Problem | Solution | Score: 10
• itertoolsproduct() | Problem | Solution | Score: 10
• itertoolscombinations() | Problem | Solution | Score: 10
• itertoolspermutations() | Problem | Solution | Score: 10
• itertoolscombinations_with_replacement() | Problem | Solution | Score: 10
• Compress the String! | Problem | Solution | Score: 20
• Iterables and Iterators | Problem | Solution | Score: 40
• Maximize It! | Problem | Solution | Score: 50
• collectionsCounter() | Problem | Solution | Score: 10
• DefaultDict Tutorial | Problem | Solution | Score: 20
• Collections namedtuple() | Problem | Solution | Score: 20
• Collections OrderedDict() | Problem | Solution | Score: 20
• Word Order | Problem | Solution | Score: 50
• Collections deque() | Problem | Solution | Score: 20
• Company Logo | Problem | Solution | Score: 30
• Piling Up! | Problem | Solution | Score: 50
• Calendar Module | Problem | Solution | Score: 10
• Time Delta | Problem | Solution | Score: 30
• Exceptions | Problem | Solution | Score: 10
• Incorrect Regex | Problem | Solution | Score: 20
• Arrays | Problem | Solution | Score: 10
• Shape and Reshape | Problem | Solution | Score: 20
• Revising the Select Query I | Problem | Solution | Score: 10
• Revising the Select Query II | Problem | Solution | Score: 10
• Select All | Problem | Solution | Score: 10
• Select By ID | Problem | Solution | Score: 10
• Japanese Cities' Attributes | Problem | Solution | Score: 10
• Japanese Cities' Names | Problem | Solution | Score: 10
• Weather Observation Station 1 | Problem | Solution | Score: 15
• Weather Observation Station 3 | Problem | Solution | Score: 10
• Weather Observation Station 4 | Problem | Solution | Score: 10
• Weather Observation Station 5 | Problem | Solution | Score: 30
• Weather Observation Station 6 | Problem | Solution | Score: 10
• Weather Observation Station 7 | Problem | Solution | Score: 10
• Weather Observation Station 8 | Problem | Solution | Score: 15
• Weather Observation Station 9 | Problem | Solution | Score: 10
• Weather Observation Station 10 | Problem | Solution | Score: 10
• Weather Observation Station 11 | Problem | Solution | Score: 15
• Weather Observation Station 12 | Problem | Solution | Score: 15
• Higher Than 75 Marks | Problem | Solution | Score: 15
• Employee Names | Problem | Solution | Score: 10
• Employee Salaries | Problem | Solution | Score: 10
• Type of Triangle | Problem | Solution | Score: 20
• The PADS | Problem | Solution | Score: 30
• Binary Tree Nodes | Problem | Solution | Score: 30
• Revising Aggregations - The Count Function | Problem | Solution | Score: 10
• Revising Aggregations - The Sum Function | Problem | Solution | Score: 10
• Revising Aggregations - Averages | Problem | Solution | Score: 10
• Average Population | Problem | Solution | Score: 10
• Japan Population | Problem | Solution | Score: 10
• Population Density Difference | Problem | Solution | Score: 10
• The Blunder | Problem | Solution | Score: 15
• Top Earners | Problem | Solution | Score: 20
• Weather Observation Station 2 | Problem | Solution | Score: 15
• Weather Observation Station 13 | Problem | Solution | Score: 10
• Weather Observation Station 14 | Problem | Solution | Score: 10
• Weather Observation Station 15 | Problem | Solution | Score: 15
• Weather Observation Station 16 | Problem | Solution | Score: 10
• Weather Observation Station 17 | Problem | Solution | Score: 15
• Weather Observation Station 18 | Problem | Solution | Score: 25
• Weather Observation Station 19 | Problem | Solution | Score: 30
• Weather Observation Station 20 | Problem | Solution | Score: 40
• Asian Population | Problem | Solution | Score: 10
• African Cities | Problem | Solution | Score: 10
• Average Population of Each Continent | Problem | Solution | Score: 10
• The Report | Problem | Solution | Score: 20
• Day 0 - Mean, Median, and Mode | Problem | Solution | Score: 30
• Day 0 - Weighted Mean | Problem | Solution | Score: 30
• Day 1 - Quartiles | Problem | Solution | Score: 30
• Day 1 - Interquartile Range | Problem | Solution | Score: 30
• Day 1 - Standard Deviation | Problem | Solution | Score: 30
• Day 4 - Binomial Distribution I | Problem | Solution | Score: 30
• Day 4 - Binomial Distribution II | Problem | Solution | Score: 30
• Day 4 - Geometric Distribution I | Problem | Solution | Score: 30
• Day 4 - Geometric Distribution II | Problem | Solution | Score: 30
• Day 5 - Poisson Distribution I | Problem | Solution | Score: 30
• Day 5 - Poisson Distribution II | Problem | Solution | Score: 30
• Day 5 - Normal Distribution I | Problem | Solution | Score: 30
• Day 5 - Normal Distribution II | Problem | Solution | Score: 30
• Day 6 - The Central Limit Theorem I | Problem | Solution | Score: 30
• Day 6 - The Central Limit Theorem II | Problem | Solution | Score: 30
• Day 6 - The Central Limit Theorem III | Problem | Solution | Score: 30

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HackerRank Breaking the Records problem solution

In this Breaking the Records problem you have Given the scores for a season, determine the number of times Maria breaks her records for most and least points scored during the season.

Problem solution in Python programming.

Problem solution in Java Programming.

Problem solution in c++ programming., problem solution in c programming., problem solution in javascript programming..

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PHP codes $min = $scores[0]; $max = $scores[0]; $min_score = 0; $max_score = 0; for($i=1; $i < count($scores); $i++){ if($scores[$i] < $min){ $min = $scores[$i]; $min_score++; } if($scores[$i] > $max){ $max = $scores[$i]; $max_score++; } } echo $max_score.' '.$min_score;

def breakingRecords(scores): minn = 0 maxx = 0 m = [] x = [] for e in scores: if m == [] and x == []: minn += 0 maxx += 0 m.append(e) x.append(e) elif e > max(x): maxx += 1 x.append(e) m.append(min(m)) elif e < min(m): minn += 1 m.append(e) x.append(max(x)) else: m.append(min(m)) x.append(max(x)) print("*************************") print("score - "+str(e)) print("minArr - "+str(m)) print("maxArr - "+str(x)) print("CurrentMin - "+str(min(m))) print("CurrentMax - "+str(max(x))) print("minCount - "+str(minn)+" maxCount - "+str(maxx)) print("*************************") return [maxx, minn]

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4 Ways to Solve FizzBuzz in Python

The technical interview is hard to master and can be a nerve-racking experience. Not only do you need to know what you are talking about, but you also have to prove it to the person interviewing you. Fortunately, most fears of failure in this regard are exaggerated, and often, the interview will boil down to only a few potentially difficult questions.

4 Methods for Solving FizzBuzz in Python

• Conditional statements.
• String concatenation.

One very common problem that programmers are asked to solve in technical interviews and take-home assignments is the FizzBuzz problem . FizzBuzz is a word game designed for children to teach them about division. In the game, each number divisible by three will be returned with a Fizz and any number divisible by four will return a Buzz . I was never a big fan of the test, but it can help weed out weaker applicants.

While the test is pretty easy to pass so long as you know the right operators, there are a variety of different ways to solve it. However, some solutions might prove to be more impressive than others, and I think this is something to keep in mind when working on this problem for a real interview. In addition to demonstrating these alternative methods of solving FizzBuzz, we are going to time each solution and compare the respective results.

How to Solve FizzBuzz in Python

1. conditional statements.

The most popular and well-known solution to this problem involves using conditional statements . For every number in n, we are going to need to check if that number is divisible by four or three. If the number is divisible by three, it will print Fizz ; if the number is divisible by four, it will print Buzz . The key here is simply knowing what operators to use to check for divisibility. In Python , we can use the modulus operator, % .

In computing, the modulo operation is meant to return the signed remainder of division. If a number is divisible by another, the remainder will always be zero, so we can use that to our advantage whenever we make our FizzBuzz function. We will structure condition blocks like this, where num is the iteration in a list of numbers.

We can now build an iterative loop following the same principle, except we’ll be adding Fizz and Buzz :

Ace Your Interview Expect These Questions in Your Second-Stage Software Developer Interview

2. String Concatenation

Though incredibly similar to its regular conditional loop counterpart, the string concatenation method is another really great way to solve this problem. Of course, this method is also all but too similar to the conditional method. The significant difference here is that the conditionals are simply going to be affecting a small sequence of characters put into the string data-type.

3. Itertools

Another way we could approach this problem — as well as other iteration problems — is to use the standard library tool, itertools . This will create a loop with better performance than most other iteration methods. Itertools can be thought of as an iteration library that is built to mirror several other extremely performant libraries from other languages, except using pythonic methods for solving problems.

Itertools will need to be imported, however, it is in the standard library. This means pip won’t be necessary, but itertools is still considered a project dependency. We are going to utilize three different methods from this module:

• cycle() : Cycle is a function takes a basic data-type and creates an iterator out of it. This function is useful and makes building custom iterators incredibly easy in Python.
• count() : Count is another generator that iterates a range. This iterator is often called an “infinite iterator,” which basically means that the count() function could essentially loop on and on forever.
• islice() :  The islice  function is short for “iteration slice.” We can use this iterator to cut out particular elements in a data structure and iterate them.

Combining these methods will allow us to create a new function where we can solve the FizzBuzz problem without using the typical iteration methods in Python that we might be used to.

The benefits of using this methodology is that the itertools library’s methods of iteration are typically going to be a lot faster than the pythonic methods of iteration. While itertools is still pythonic, it is likely that the speed of iterative looping is going to improve when using this library over the typical for loop in Python. Needless to say, creating a faster algorithm than any other applicant could certainly put you on the map for getting the job. This is a valuable module and application of the module for programmers who are still searching for employment.

Another method we could use to solve this problem is even more Pythonic of a solution, and it makes use of Python’s bridge to scientific computing, lambda . There are a lot of standard functions that can be used with these lambda expressions, and they certainly come in handy. One of the most frequently used methods in this regard is the map() method. This method is going to take an expression that we can create using lambda as well as an iterative data structure.

For this example, I used the range generator, and the “not” keywords in order to reverse the polarity of the modulus operators usage.

Ace Your Interview 15 Good Questions to Ask in an Interview

What’s the Best Way to Solve FizzBuzz in Python?

With all of these new ways to solve the problem, you might be wondering which one you should use. Of course, there are going to be trade-offs between the solutions, but in order to really make a great impression, we could narrow our decision down to using either the lambda method or the itertools method.

The lambda method has the advantage of being incredibly concise. However, depending on what code the map() method uses for iteration, it might trail behind the itertools method in terms of speed due to its less efficient iteration. The only way to figure out whether or not this is the case is to run some tests and compare our interpreter return times. So, that is going to be the mission between comparing these two heaps of code. In order to facilitate this comparison, I am going to be using the IPython magic in-line command, %timeit . Let’s start by trying it out on the itertools method. Since I wrote this as a function earlier, I can simply time the function call:

We will do the same with the lambda method:

Just as I predicted, the itertools method came in just a little faster, while the lambda method lagged slightly behind losing less than a millisecond off of the overall interpretation time. The answer here is somewhat of a mixed bag because the concise nature of the lambda expression and map() function in tandem make the lambda method appear to be a lot more impressive. But the compile time of the itertools method is most certainly impressive because of its speed.

As is often the case in programming, there are multiple ways to do one thing, and as is also often the case, some ways are significantly better than others. There are certainly some trade-offs depending on what methodology you select, but this is what defines your own style as a programmer. I believe regardless  of the decision that is made, using these faster methods will almost certainly make any aspiring programmer look a lot more proficient in their take home assignment. Furthermore, any aspiring programmer could certainly learn a lot more about programming and the language they are programming in by trying out different methods of doing the same thing. 

Built In’s expert contributor network publishes thoughtful, solutions-oriented stories written by innovative tech professionals. It is the tech industry’s definitive destination for sharing compelling, first-person accounts of problem-solving on the road to innovation.

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csglitz_python_assignments

These are the assignments from my free python tutorial series on youtube Free Python 3 Course (from Beginner to Expert)

In order to solve assignments in correct order check the description of my tutorials. After each tutorial upload, I'll update the description and add the questions related to that tutorial.

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Mastering Rank Function in Python Pandas: A Complete Guide

• Introduction

In the realm of data analysis and manipulation, Python's Pandas library stands out for its robust functionality and ease of use. Among its powerful features, the rank function is a vital tool for sorting and ranking data efficiently. This guide aims to provide an in-depth understanding of the rank function, ensuring you can leverage its full potential in your data analysis projects.

• Key Highlights

Comprehensive overview of the rank function in Python Pandas

Step-by-step instructions on using the rank function for different types of data

Best practices for optimizing data ranking and sorting

Advanced techniques for custom ranking

Real-world examples to illustrate the application of the rank function

• Mastering the Rank Function in Python Pandas

Before we dive into the world of data analysis with Python's Pandas library, understanding the intricacies of the rank function is essential. This powerful tool sorts and ranks data, providing insights that are pivotal for data analysis. In this section, we unravel the syntax, parameters, and the unique edge the rank function holds over other sorting functions, laying a solid foundation for its practical applications.

Introduction to the Rank Function

The rank function in Pandas is a versatile tool, pivotal for data analysis across various domains. At its core, the function assigns ranks to data, based on their value, from the smallest to the largest. This functionality is not just limited to numeric data; it extends to categorical and date data as well, making it a cornerstone for data scientists. For instance, in e-commerce, ranking product sales can unveil best-sellers, guiding inventory decisions. Similarly, in finance, ranking investment returns can spotlight high-performing assets.

The rank function's flexibility and wide applicability underscore its importance in the Pandas library, positioning it as a go-to solution for sorting and ranking tasks.

Syntax and Parameters

Diving deeper into the rank function, its syntax is straightforward yet powerful, DataFrame.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) , which opens a plethora of possibilities for data manipulation. Here's a breakdown:

• axis : Determines whether to rank by rows or columns.
• method : Dictates how to handle ties (e.g., 'average', 'min', 'max').
• numeric_only : Specifies if only numeric columns should be considered.
• na_option : Deals with missing values, either by keeping, removing, or placing them last.
• ascending : Defines the ranking order.
• pct : When set to True, ranks are expressed as percentile ranks.

For instance, ranking a sales dataset while handling ties by their minimum rank could be as simple as sales_data.rank(method='min') . This granular control allows for tailored data analysis, accommodating diverse datasets and requirements.

Comparison with Other Sorting Functions

While Pandas offers a suite of sorting functions, the rank function distinguishes itself with its nuanced approach to data ranking. Unlike sort_values() , which merely sorts data, or sort_index() , which organizes data based on the index, the rank function provides a detailed hierarchy of data points, essential for in-depth analysis.

Consider a dataset of marathon runners where multiple runners finish with the same time. While sort_values() could organize them by finishing time, rank() delves deeper, assigning ranks based on the chosen method for handling ties, offering insights into their performance relative to each other. This capability to dissect and understand the position of each data point within a larger dataset underscores the rank function's superiority for complex analytical tasks.

• Implementing Rank in Various Scenarios

Diving into the practical sphere, the Python Pandas' rank function unfolds its prowess across diverse scenarios, ranging from numeric and categorical to date and time data management. This section, with a professional tone, peels back the layers on its versatile applications, providing insightful examples and guidelines. Let's embark on this journey to master the art of ranking data effectively, ensuring your data analysis skills are not just proficient but exceptional.

Mastering Numeric Data Ranking

Understanding the Basics Before we delve into examples, it's crucial to grasp that ranking numeric data allows us to order data from the smallest to the largest values, or vice versa. Handling ties and missing values strategically ensures integrity in our data analysis.

Practical Application Consider a dataset, df , with a column Sales .

This simple yet powerful operation assigns ranks starting from 1. Note, by default, it handles ties by assigning the average rank.

Handling Ties and Missing Values To manage ties more effectively, consider using the method parameter:

This method ensures that tied values receive the minimum possible rank, enhancing fairness and clarity in your analysis.

Elevating Categorical Data Ranking

The Challenge with Categories Categorical data, inherently qualitative, poses unique challenges. Transforming these categories into ranks not only quantifies the qualitative but also simplifies complex datasets.

From Categories to Ranks Imagine a dataset, df , with a column Category having values 'High', 'Medium', and 'Low'.

In this approach, we manually convert categories to ranks, facilitating a more nuanced analysis.

Dealing with Large Datasets For extensive datasets, efficiency becomes key. Utilizing Pandas' astype method to convert data types or applying vectorized operations can significantly enhance performance.

Ranking Date and Time Data

Navigating Through Time Ranking date and time data introduces a compelling dimension to data analysis, allowing us to sequence events chronologically and uncover trends over time.

Effective Strategies Consider a DataFrame, df , with a DateTime column.

This example illustrates how to assign ranks to dates, aiding in the chronological analysis of events. Handling different formats and time zones may require additional steps, such as standardizing to UTC with Pandas' tz_convert .

Advanced Tip: Utilize pd.to_datetime for converting strings to datetime objects efficiently, ensuring all data is in a compatible format for ranking.

• Mastering Advanced Ranking Techniques in Python Pandas

Moving beyond the elementary use of the rank function, this section unfolds the sophisticated terrain of advanced ranking techniques in Python Pandas. Here, we not only aim to equip you with the knowledge of custom ranking methods and multi-level ranking but also ensure you're well-prepared to tackle complex data analysis challenges with confidence. Let's dive deeper into these advanced techniques, adding more tools to your data analysis arsenal.

Creating Custom Ranking Methods in Pandas

Why Custom Ranking?

Sometimes, the default ranking methods ( 'average' , 'min' , 'max' , etc.) offered by Pandas do not meet specific analytical needs. In such cases, crafting a custom ranking method becomes imperative. Custom ranking allows for flexibility and creativity in handling unique data scenarios.

Practical Application:

Let's consider you're analyzing a dataset of sales performance and want to rank salespersons not only by their sales but also by the number of deals closed, with a custom weight assigned to each criterion.

In the example above, we used a lambda function to define our custom ranking criteria, showcasing a straightforward approach to implement bespoke ranking logic.

Mastering Multi-Level Ranking in Pandas

Expanding Your Ranking Horizons

Multi-level ranking, a method that allows for ranking within hierarchical data structures, is essential when dealing with complex datasets. It enables the analysis of data at multiple granularity levels, providing deeper insights.

Practical Example:

Imagine a dataset containing sales data across multiple regions, with each region having multiple salespersons. The goal is to rank salespersons within each region based on their sales.

This example illustrates how to perform multi-level ranking in Pandas, a technique that proves incredibly useful for nuanced analysis across different segments or categories within your data.

• Optimizing Performance with Pandas Rank Function

In the world of data analysis, efficiency is key. When working with large datasets, the computational intensity of ranking operations can become a bottleneck. This section explores practical strategies to optimize the performance of the Pandas rank function, ensuring data processing is both efficient and effective. By applying these techniques, you can enhance your data analysis workflow, saving time and resources.

Minimizing Memory Usage in Pandas

Tips and Tricks for Efficient Memory Management

Utilize category data type : When working with categorical data, convert the datatype to 'category'. This significantly reduces memory usage, especially for datasets with a large number of categories. For example, df['column_name'] = df['column_name'].astype('category') can make a big difference.

In-Place Operations : Whenever possible, use in-place operations to modify data. This avoids creating unnecessary copies of data. For instance, using df.sort_values(by='column', inplace=True) instead of df = df.sort_values(by='column') can save memory.

Data Type Conversions : Be mindful of the data types in your DataFrame. Converting float64 to float32 or int64 to int32, when precision is not crucial, can lead to substantial memory savings. For example, df['float_column'] = df['float_column'].astype('float32') .

By implementing these strategies, you can make your data analysis processes more memory-efficient, enabling smoother and faster operations on large datasets.

Accelerating Pandas Ranking Operations

Strategies to Enhance Performance

Parallel Processing : Leveraging parallel processing can significantly speed up ranking operations. Libraries such as Dask allow you to easily parallelize your Pandas operations, including ranking. For an introduction to Dask and parallel computing with Pandas, check out Dask's official documentation .

Chunking Large Datasets : Breaking your dataset into smaller chunks can make ranking operations more manageable and faster. Process each chunk separately and then combine the results. This method is particularly useful when dealing with datasets that are too large to fit into memory.

Efficient Sorting Before Ranking : Sorting your data by the relevant columns before applying the rank function can sometimes improve performance, especially if your dataset is nearly sorted. Pandas can take advantage of the sorted order to optimize the ranking operation.

Implementing these strategies can drastically reduce the time required for ranking operations, making your data analysis tasks more efficient. Embracing parallel processing and smart data management techniques are key to optimizing performance in data-intensive environments.

• Real-world Applications of Pandas Rank Function

In the realm of data analysis, the rank function in Python's Pandas library is a powerful tool that finds application across various industries. This section delves into the practical, real-world uses of this function, showcasing its versatility and impact. Through detailed case studies in e-commerce and finance, we illuminate how professionals leverage the rank function to glean insights, optimize operations, and make data-driven decisions.

Case Study: E-commerce Analytics

Understanding Customer Behavior through Ranking

In the competitive e-commerce landscape, understanding and predicting customer behavior is paramount. An e-commerce giant harnessed the power of Pandas' rank function to analyze massive datasets of customer interactions. Here's how they did it:

Sales Data Analysis : By ranking products based on sales figures, the company identified top-performing and underperforming products. This insight helped in inventory optimization and marketing strategies.

Customer Lifetime Value (CLV) : They ranked customers based on their lifetime value, enabling targeted marketing campaigns. Customers with higher ranks received personalized offers, enhancing customer loyalty.

Handling Ties : In cases where customers had identical purchase frequencies, the rank function's method='average' parameter ensured fair ranking, thus maintaining data integrity.

This strategic application of ranking not only streamlined operations but also bolstered sales strategies, proving the indispensable value of the rank function in e-commerce analytics.

Case Study: Finance Sector Insights

Risk Assessment and Portfolio Management through Ranking

In finance, risk assessment and portfolio management are critical. A leading financial institution utilized Pandas' rank function to elevate their data analysis processes. Let's explore their approach:

Credit Risk Evaluation : By ranking clients based on their credit scores and financial behaviors, the institution could prioritize high-risk accounts for further review. This proactive approach mitigated potential losses.

Portfolio Optimization : Investors' portfolios were ranked based on performance metrics such as return on investment (ROI). This ranking facilitated data-driven decisions, guiding clients towards more profitable investments.

Custom Ranking Methods : For nuanced financial analyses, custom ranking methods were developed using lambda functions. This allowed for flexibility in ranking criteria, catering to specific analytical needs.

The finance sector's application of the rank function underscores its importance in managing risks and optimizing investment strategies, thereby showcasing the rank function's critical role in financial analytics.

The rank function in Python's Pandas library is an indispensable tool for data analysts and scientists. Its versatility and efficiency in sorting and ranking data make it a go-to method for insightful analysis. By mastering the rank function, as detailed in this guide, you can enhance your data processing capabilities and unlock new possibilities in your projects.

Q: What is the rank function in Pandas?

A: The rank function in Pandas is used to rank items in a series or DataFrame. It sorts data based on their values, assigning ranks from the smallest to the largest value, with various options for handling ties.

Q: How does the rank function handle ties?

A: Pandas' rank function handles ties through its method parameter. Options include average (default) to assign the average rank to tied values, min to assign the minimum rank, max for the maximum, first to rank ties based on their order in the data, and dense to increase the rank by 1 between groups.

Q: Can you rank categorical data with Pandas' rank function?

A: Yes, you can rank categorical data using the rank function in Pandas. It involves converting the categories to a numerical scale that reflects their rank order, often requiring preprocessing steps like mapping categories to numbers.

Q: Is it possible to perform custom ranking in Pandas?

A: Yes, Pandas allows for custom ranking. This can be achieved by applying functions, such as lambda functions, to your data before ranking, or by using the sort_values method along with rank to tailor your ranking criteria.

Q: How do I optimize the performance of the rank function for large datasets?

A: To optimize performance, consider converting data to a more efficient dtype before ranking, using in-place operations when possible, and leveraging parallel processing or chunking techniques to handle large datasets more efficiently.

Q: What are some real-world applications of the rank function?

A: The rank function is widely used in various domains, such as e-commerce for customer segmentation, finance for risk assessment, sports analytics for player rankings, and any field requiring sorted data analysis for decision-making.

Q: Can the rank function handle missing values?

A: Yes, the rank function in Pandas can handle missing values. By default, missing values are assigned a rank that is one greater than the highest rank, but this behavior can be modified with the na_option parameter.

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A Comprehensive Guide to Pandas rank(): Understanding and Utilizing Data Ranking in Python

Data analysis is a critical aspect of any field that deals with data, and Python has a powerful library called Pandas that simplifies the process of data manipulation and analysis. Among the many functions Pandas offers, rank() is an essential tool that allows us to assign a rank to data elements based on their values. In this tutorial, we will explore the intricacies of the rank() function, understand its parameters, and provide you with practical examples to grasp its functionality.

Table of Contents

• Introduction to Pandas rank()
• Parameters of the rank() Function
• Understanding Tie Handling
• Examples of Using rank()

Example 1: Ranking Exam Scores

Example 2: handling ties in ranking olympic medals, 1. introduction to pandas rank().

Pandas is an open-source data manipulation and analysis library for Python. It provides powerful tools for working with structured data, including the rank() function, which helps assign ranks to data based on their values. The ranking process involves assigning unique integer values to data elements based on their order. Higher values are given to larger data elements, indicating a higher rank.

The basic syntax of the rank() function is as follows:

Before diving into the syntax and parameters, let’s take a closer look at the parameters that influence the ranking process.

2. Parameters of the rank() Function

The rank() function in Pandas accepts several parameters that allow you to customize the ranking process to suit your data analysis needs:

• axis : Specifies whether the ranking should be performed along the rows ( axis=0 ) or columns ( axis=1 ).
• method : Determines how to handle tied values. Options include 'average' , 'min' , 'max' , and 'first' .
• numeric_only : If True , only numeric columns will be ranked.
• na_option : Determines how to treat missing values. Options are 'keep' , 'top' , and 'bottom' .
• ascending : If True , higher values will receive higher ranks; if False , the opposite is true.

3. Understanding Tie Handling

Tied values are values that have the same value and are assigned the same rank. The method parameter in the rank() function allows you to specify how to handle ties:

• 'average' : Tied values receive the average of the ranks they would have been assigned. This is the default method.
• 'min' : Tied values receive the lowest rank that they would have been assigned.
• 'max' : Tied values receive the highest rank that they would have been assigned.
• 'first' : Tied values receive the lowest rank, and subsequent ranks are incremented by the number of tied values.

Tie handling is crucial in ensuring that your ranking results reflect the actual data relationships appropriately.

4. Examples of Using rank()

In this section, we will walk through two practical examples to demonstrate how the rank() function works.

Let’s consider a scenario where we have a DataFrame containing students’ names and their corresponding exam scores. We want to rank the students based on their scores.

To rank the students based on their scores, we can use the following code:

In this example, the ascending=False parameter ensures that higher scores receive higher ranks. The resulting DataFrame will look like this:

Consider a scenario where we have data about countries and the number of gold medals they won in the Olympics. We want to rank the countries based on their gold medal counts and handle tied ranks using the 'min' method.

To rank the countries based on their gold medal counts and handle tied ranks, we can use the following code:

In this example, the method='min' parameter ensures that tied ranks receive the lowest possible rank. The resulting DataFrame will look like this:

5. Conclusion

In this tutorial, we have explored the Pandas rank() function, a powerful tool for assigning ranks to data elements based on their values. We discussed the parameters of the rank() function, including axis , method , numeric_only , na_option , and ascending . We also delved into tie handling methods, including 'average' , 'min' , 'max' , and 'first' .

Through practical examples, we demonstrated how to use the rank() function to rank exam scores and Olympic medals. By following these examples and understanding the concepts behind the rank() function, you are well-equipped to apply ranking techniques to your own data analysis tasks. Remember that proper ranking can provide valuable insights into the relationships within your data and help you make informed decisions based on the ranked results.

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Answer to Question #224608 in Python for kaavya

List Indexing - 3

Given N numbers, and an index, write a program to store the numbers in a list and print the number at given index. For this problem, each input will contain T test cases. Each test case will give an index Ki as input, which should be considered to print the number.

The first line of input is an integer N. The second line of input is an integer T representing the number of test cases. The next N lines contain integers representing the numbers of the list. The next T lines contain integer Ki for each line.

You need to print a number in a new line for each of the K test cases.

Explanation:

In the given example, we are given

4 numbers 1, 2, 3, 4 as input For the first test case, K=0, the number at 0th index is 1. For the second test case, K=3, the number at 3rd index is 4. So, the output should be

Sample Input 1

Sample Output 1

Sample Input 2

Sample Output 2

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5 Best Ways to Rank Rows in a Pandas DataFrame with Python

💡 Problem Formulation: When working with tabular data, analysts frequently need to rank rows based on the values in a certain column to identify hierarchies or prioritize information. For instance, one might have a DataFrame of sales data and want to rank rows based on the total sales amount to quickly identify top-performing products. The desired output is a new column in the DataFrame that shows the rank of each row in comparison to others.

Method 1: Rank with rank() Method

The DataFrame.rank() method in pandas provides an effective way to rank rows based on a column’s values. It supports different ranking methods like ‘average’, ‘min’, ‘max’, ‘first’, and ‘dense’. The default behavior assigns a rank with an average for tie scores.

Here’s an example:

This code snippet generates a DataFrame with a ‘Sales’ column and ranks it using the default ranking method. Tied values receive the same rank, which is the average of their positions in the ordered list.

Method 2: Ranking with a Custom Order

With pandas, you can rank your DataFrame rows in both ascending and descending order using the ascending parameter of the rank() method to reverse the ranking.

This snippet adds a ‘Rank_desc’ column to the DataFrame using the rank() method with ascending set to False, which results in a descending rank where the highest sales get the rank of 1.

Method 3: Rank by Group with groupby() and rank()

Pandas allows you to combine groupby() and rank() methods to assign ranks within groups. This is helpful when you want to compare items within categories.

In this example, a ‘Category’ column is added, and ‘Group_Rank’ is created by ranking ‘Sales’ within each category. This demonstrates that the rank is calculated separately for each group.

Method 4: Assigning Ranks Using Ties Policies

In pandas, you can control how to handle ties in the ranking by specifying the method parameter in the rank() function. For example, ‘first’ will assign ranks in the order the values appear in the data.

This code adds a ‘Rank_first’ column, which assigns ranks to ‘Sales’ using the ‘first’ ties policy, breaking ties based on their order in the DataFrame.

Bonus One-Liner Method 5: Lambda Function with Rank

For more complex ranking logic, you can apply a lambda function that uses the rank() method. This is useful for inline calculations.

The lambda function in this snippet is applied across each row, using the rank() method to provide an inline ranking. This is a versatile method that can be tailored to more complex ranking conditions.

Summary/Discussion

• Method 1: Rank with rank() method. Simple and straightforward. Handles ties using average ranking. Limited customization in handling ties.
• Method 2: Ranking with a Custom Order. Allows ascending or descending ranking. Easy-to-use. Can’t customize beyond the order.
• Method 3: Rank by Group with groupby() and rank() . Useful for categorical data comparison. More complex. Requires understanding of groups.
• Method 4: Assigning Ranks Using Ties Policies. Customizes handling of ties. Offers several methods. Ties policy choice may not be intuitive for all users.
• Bonus Method 5: Lambda Function with Rank. Highly customizable. Ideal for inline operations. May impact readability and performance for complex data sets.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

Computing data ranks in Pandas DataFrame (5 examples)

Introduction.

Working with data often requires ordering and ranking based on certain criteria. Pandas, a powerful and widely-used Python library for data manipulation, provides an intuitive way to rank data within DataFrames. Ranking plays a crucial role in data analysis, helping to identify trends, anomalies, or relationships among data. This tutorial aims to guide you through various examples of computing data ranks in Pandas DataFrames, catering to beginners and advanced users alike.

Ranking in Pandas

Before diving into examples, it’s crucial to understand how ranking in Pandas works. The .rank() method in Pandas is used to compute numerical data ranks (1 through n) along an axis. By default, equal values are assigned a rank that is the average of the ranks of those values. However, this behavior can be customized using the method parameter.

Available ranking methods include:

• average : Default. Assigns the average rank to tied values.
• min : Assigns the minimum rank to tied values.
• max : Assigns the maximum rank to tied values.
• first : Ranks items by their order of appearance in the data.
• dense : Similar to min , but the ranks always increase by 1 between groups.

Different data types and structures may require different approaches to ranking, which we will explore in the examples below.

Example 1: Basic Ranking

This example demonstrates the most straightforward ranking in a single DataFrame column.

In this example, the scores 90 and 85 are tied, thus receive the average of their ranks (4.5 and 2.5, respectively), showcasing the default average ranking method.

Example 2: Custom Ranking Method

Here, we apply a different ranking method to handle ties differently.

This time, using the min method, tied values receive the minimum possible rank, illustrating how choosing a ranking method affects the output.

Example 3: Ranking with Missing Values

Handling missing values is an essential aspect of data manipulation. Here, we show how Pandas deals with NaN values in ranking.

NaN values are excluded from the ranking, emphasizing the need to clean or impute missing values before performing ranking for analysis completeness.

Example 4: Ranking Across Multiple Columns

Advanced use cases may involve ranking data across multiple columns. This example demonstrates ranking students by multiple performance metrics.

This method calculates an average score for each row (student) and then ranks them, offering a way to compare multidimensional data.

Example 5: Ranking with Custom Functions

The power of Pandas ranking extends with the ability to use custom functions for more complex scenarios, such as weighted averages.

Combining Python’s flexibility with Pandas ranking capabilities allows for tailored ranking methods, such as the weighted ranking shown above.

Understanding and implementing data ranking in Pandas opens up numerous possibilities for data analysis and insight generation. The examples provided in this tutorial illustrate the versatility and power of Pandas for addressing a wide range of ranking needs, from the most basic to more complex, customized scenarios. Empowered with this knowledge, you are well-equipped to explore your data’s hierarchical structure and derive meaningful conclusions.

Next Article: Pandas: Convert a list of dicts into a DataFrame

Previous Article: Using DataFrame.quantile() method in Pandas (5 examples)

Series: DateFrames in Pandas

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Python is a great language for data analysis , primarily because of the fantastic ecosystem of data-centric Python packages . Pandas is one of those packages and makes importing and analyzing data much easier. 

Pandas DataFrame rank() method returns a rank of every respective entry (1 through n) along an axis of the DataFrame passed. The rank is returned based on position after sorting.

Syntax: DataFrame.rank(axis=0, method=’average’, numeric_only=None, na_option=’keep’, ascending=True, pct=False) Parameters: axis: 0 or ‘index’ for rows and 1 or ‘columns’ for Column. method: Takes a string input(‘average’, ‘min’, ‘max’, ‘first’, ‘dense’) which tells pandas what to do with same values. Default is average which means assign average of ranks to the similar values.  numeric_only: Takes a boolean value and the rank function works on non-numeric value only if it’s False.  na_option: Takes 3 string input(‘keep’, ‘top’, ‘bottom’) to set position of Null values if any in the passed Series.  ascending: Boolean value which ranks in ascending order if True. pct: Boolean value which ranks percentage wise if True.  Return type: Series with Rank of every index of caller series.

Let’s see some examples of how to check the rank of DataFrame data using dataframe.rank() method of the Pandas library .

Ranking Column with Unique values In the following example, a new rank column is created which ranks the Name of every Player. All the values in the Name column are unique and hence there is no need to describe a method.

As shown in the image, a column ‘ rank ‘ was created with the rank of every Name. After the sort_value function sorted the DataFrame for names, it can be seen that the rank was also sorted since those were ranking of Names only.

Before Sorting-

After Sorting-

Sorting Column with some similar values in the following example, DataFrame is first sorted for ‘ team name’ and first the method is the default (i.e. average), and hence the rank of same Team players is average. After that min method is also used to see the output.

With method=’average’

With method=’min’

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